提高COPD诊断的机器学习:对分类算法进行比较分析
Walaa H Elashmawi1,2, Adel Djellal3, Alaa Sheta4
1Department of Computer Science, Suez Canal University, Ismailia 41522, Egypt.
Diagnostics (Basel, Switzerland)
|January 8, 2025
概括
机器学习模型准确地诊断出慢性阻塞性肺病 (COPD),这是美国的一个主要健康问题. 随机森林分类器实现了最高的准确性,显示COPD早期检测的希望.
科学领域:
- 肺部医学 肺部医学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 慢性阻塞性肺病 (COPD) 是美国的主要死亡原因.
- 慢性肺炎是一种慢性炎症性肺病,导致气流阻塞和呼吸困难和咳等症状.
- 慢性肺炎患者面临着诸如心脏病和肺癌等并发症的风险增加.
研究的目的:
- 评估各种机器学习 (ML) 分类器在诊断COPD方面的有效性.
- 为了比较不同ML算法的性能,从患者数据中识别COPD.
主要方法:
- 这项研究使用了1603名患者的数据集,用于进行肺功能测试.
- 应用了10个机器学习分类器:逻辑回归,梯度提升分类器,支持矢量机,高斯素朴贝叶斯,随机森林分类器,K-最近邻居分类器,决策树和人工神经网络.
- 模型性能使用准确度,F-score和ROC值进行评估.
主要成果:
- 随机森林分类器 (RFC) 显示了最高的准确性,在培训中达到82.06%,在测试中达到70.47%.
- 此外,RFC还实现了最大的F分数和0.82.8的ROC值.
- 模型的整体准确度在67.81%至82.06% (培训) 和66.73%至71.46% (测试) 之间.
结论:
- 机器学习模型显示了COPD诊断的巨大潜力.
- 随机森林分类器成为识别COPD的高效工具.
- 这些发现与之前的研究一致,支持ML在呼吸系统疾病诊断中的实用性.
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